Mental, neurological, and substance use disorders are common, highly disabling, and associated with premature mortality, posing a significant burden on patients, families, and society at large. These disorders are often based on subjective and qualitative patient statements in a written descriptive format. Modern Artificial Intelligence (AI) algorithms and approaches are continuously refined and implemented with outstanding performance in healthcare disciplines, providing new hope to address the challenge of undiagnosed and untreated mental, neurological, and substance use disorders. Mental health practitioners and neurologists can take advantage of cutting-edge AI tools to analyze unstructured data to identify patients at the earlier or prodromal stage which may improve the effectiveness of the treatment. Furthermore, these tools have the potential to personalize treatment based on the patient's unique characteristics.
The focus of this Research Topic is to present insight into the theory of AI models for diagnosis and treatment of mental, neurological, and substance use disorders, as well as the implementation, dissemination, and evaluation of the models across clinical settings. Researchers are encouraged to submit their studies focused on AI solutions for addressing real-world challenges and concerns in this area. Studies can apply various AI approaches and techniques, including machine learning, natural language processing, speech analysis, and speech recognition methods to analyze different data types, such as:
• electronic health record data;
• clinical trial data;
• genetic data;
• vocal and visual expression data;
• patient-generated data (e.g., social media, patient portal, wearable data);
• imaging data (e.g. radiology data).
Investigators should discuss the significance of AI solutions in facilitating access to healthcare information, improving healthcare quality outcomes, and reducing the cost of treatment.
Mental, neurological, and substance use disorders are common, highly disabling, and associated with premature mortality, posing a significant burden on patients, families, and society at large. These disorders are often based on subjective and qualitative patient statements in a written descriptive format. Modern Artificial Intelligence (AI) algorithms and approaches are continuously refined and implemented with outstanding performance in healthcare disciplines, providing new hope to address the challenge of undiagnosed and untreated mental, neurological, and substance use disorders. Mental health practitioners and neurologists can take advantage of cutting-edge AI tools to analyze unstructured data to identify patients at the earlier or prodromal stage which may improve the effectiveness of the treatment. Furthermore, these tools have the potential to personalize treatment based on the patient's unique characteristics.
The focus of this Research Topic is to present insight into the theory of AI models for diagnosis and treatment of mental, neurological, and substance use disorders, as well as the implementation, dissemination, and evaluation of the models across clinical settings. Researchers are encouraged to submit their studies focused on AI solutions for addressing real-world challenges and concerns in this area. Studies can apply various AI approaches and techniques, including machine learning, natural language processing, speech analysis, and speech recognition methods to analyze different data types, such as:
• electronic health record data;
• clinical trial data;
• genetic data;
• vocal and visual expression data;
• patient-generated data (e.g., social media, patient portal, wearable data);
• imaging data (e.g. radiology data).
Investigators should discuss the significance of AI solutions in facilitating access to healthcare information, improving healthcare quality outcomes, and reducing the cost of treatment.
